Extended ARMA graph neural networks for the prognosis of complex systems

With the advancement of intelligent sensing techniques, massive monitoring signals are collected and accumulated from industrial systems. Given that sensors are often correlated and constructed to reflect graph topology, the signals can be conceptualized as graph data. The polynomial filter-based graph neural networks (GNNs) are commonly employed to exploit information from nodes features and graph topology for graph data analysis. However, the polynomial filter-based GNNs encounter difficulty in accurately modeling sharp changes and the coefficients can vary a lot, making them hard to learn. To address this problem, a novel graph neural network named extended auto-regressive moving average graph neural network (eAGNN) is proposed. Compared with auto-regressive moving average (ARMA) neural network, the order restriction are removed, allowing for the inference of a more general neural network, which enables the modeling of filters with more different shapes. Furthermore, both low-frequency and high-frequency information are explicitly and separately extracted so as to alleviate the burden of the learning process and further enhance the learning capability. Finally, several experiments including public node classification and fault diagnosis were conducted. The results demonstrate that the proposed eAGNN exhibits high performance compared to alternative methods.